MLP@P- Machine Learning Physics @ Plateau
Informal meetings on statistical physics & machine learning
Organized by:
Sergio Chibbaro (LISN)
Cyril Furtlehner (LISN)
Valentina Ros (LPTMS)
Pierfrancesco Urbani (IPhT)
To subscribe to the mailing list, write to valentina.ros@cnrs.fr
Mini-workshop on Class Imbalance
When: Friday, November 15 2024, all day
Where: LPTMS, petit Amphi (1° étage)
Emanuele Francazi – A theoretical analysis of the learning dynamics under class imbalance
Stefano Sarao-Mannelli – Bias-inducing geometries: exactly solvable data model with fairness implications
Mauro Pastore – Restoring balance: principled under/oversampling of data for optimal classification
Francesco Saverio Pezzicoli – Anomaly-Detection Class Imbalance in Exactly Solvable Models
Seminar by Gabriele Sicuro, University of Bologna
When: Friday, October 4 2024, at 11:00am
Where: LISN, bat 660 salle 2014 (2° étage)
Heavy-tailed covariates in high dimensions
Machine learning theoretical models very often assume a dataset obtained from a Gaussian distribution, or from a Gaussian mixture. The possible limitations of such a Gaussian assumption have been recently object of investigation, and theoretically characterization, leading to a number of "Gaussian universality" results. In this talk I will present an analytical treatment of the performance in high dimensions of simple architectures on heavy-tailed distributed datasets, showing that even simple generalized linear models exhibit a striking dependence on non-Gaussian features in both classification and regression tasks.